Post-earthquake search and rescue operations require rapid exploration under uncertain conditions, high obstacle density and limited communication availability. In such environments multi-robot systems provide advantages in scalability and parallel exploration, yet effective coordination without centralized control remains a critical challenge. This study proposes a Multi-Component Stigmergic Search (MCSS) approach that extends conventional stigmergy-based coordination by integrating multiple environmental decision factors, including pheromone intensity, target-generated indirect signals, robot density and visitation history. The proposed approach was evaluated in a grid-based simulation environment representing rubble conditions and compared with a non-stigmergic exploration strategy and a fully random search method under different obstacle densities. Performance was assessed in terms of target discovery over time, cost per target, and average route length per target across repeated simulation runs. The results demonstrate that MCSS consistently achieves faster exploration, lower search cost, and shorter route lengths than the comparison approaches while maintaining stable performance under increasing environmental complexity. These findings suggest that combining stigmergic indirect communication with multi-component environmental guidance can improve the efficiency and robustness of autonomous multi-robot search strategies for disaster response scenarios.